We conducted experiments on three datasets, namely MNIST [1]}, Fashion MNIST (FMNIST) [2]}, and CIFAR-10 [3]}. We compare our proposed method against FedSGD, FedAvg [4]}, FedProx [5]}, and SCAFFOLD [6]} using two popular CNN models [1]}, [4]} with two convolutional layers each and number of parameters as detailed in Table REF . We do not compare against FedHybrid [9]} and FedPD [10]}, since they are both involve communication rounds that require participation of all users, which we deem unrealistic for large-scale FL scenarios. In brief, our experiments unravel three main findings:
(i) FedADMM achieves high accuracy in both IID and non-IID settings within substantially fewer rounds, which translates to large communication savings; (ii) FedADMM features robust convergence against both statistical and system heterogeneity, and requires no hyperparameter tuning across the IID and non-IID settings. This is in contrast to FedProx whose proximal coefficient needs to be carefully chosen according to data dissimilarity, which is difficult to assess in a practical scenario; (iii) FedADMM outperforms all baseline methods in all test scenarios, with the advantages being most pronounced in large-scale systems with heterogeneous data distributions. This is as opposed to other methods, where an improved performance is restricted to either the IID or the non-IID setting.
